使用VGG训练Imagenet

准备数据

具体官网地址，请点击这里

ImageNet官网

训练数据集：ILSVRC2012_img_train.tar

验证数据集：ILSVRC2012_img_val.tar

数据解压

sudo tar –xvf ILSVRC2012_img_train.tar -C ./train 
sudo tar –xvf ILSVRC2012_img_val.tar -C ./val

对于val数据集，解压以后是所有的验证集图片，共50000张，大约6.3G。

对于train数据集，解压后是1000个tar文件，每个tar文件表示1000类里的一个类，共138G，对于1000个子tar，需要再次解压，解压脚本unzip.sh如下

dir=/home/satisfie/imagenet/train #satisfie 是我的用户名for x in `ls *.tar`do    filename=`basename $x .tar`   #注意空格    mkdir $filename    tar -xvf $x -C ./$filenamedone
1
2
3
4
5
6
7
1
2
3
4
5
6
7

i7 6700K配合我的500G固态硬盘解压超快，到这原始数据就准备好了，分别放在

• /home/satisfie/imagenet/train：里面有1000个文件夹，每个文件夹下为JPG图片

• /home/satisfie/imagenet/val ：里面有验证集的50000张图片

接下来下载标签等其他说明数据~~~

下载其他数据

进入大caffe根目录，执行/data/ilsvrc12/get_ilsvrc_aux.sh下载其他数据，包括

• det_synset_words.txt
• synset_words.txt— 1000个类别的文件夹名称及真是物体的名称，比如 “n01440764 tench Tinca tinca”,在训练中，这些都当做一个类别。
• synsets.txt — 1000个类别的文件夹名称,比如”n01440764”…
• train.txt — 1000个类别每张图片的名字及其标签，比如 “n01440764/n01440764_10026.JPEG 0” 共有1281167张图片
• val.txt — 同上，总共有50000张。比如“ILSVRC2012_val_00000001.JPEG 65”
• test.txt — 同上，为测试集合,总有100000张
• imagenet_mean.binaryproto — 模型图片的各个通道均值
• imagenet.bet.pickle

模型的训练

训练数据准备

由于转化为lmdb数据库格式需要耗费较大的空间，且不支持shuffle等操作，所以这里直接读取原图片，使用的类型是ImageData，具体看下面的prototxt

其中的train_new.txt中对每张图片的加上了绝对值路径，这样才能被读取。 
使用sed命令即可，

sed 's/^/\/home\/satisfie\/imagenet\/val\/&/g' val.txt >val_new.txt
1
1

VGG_train_val.prototxt

name: "VGG_ILSVRC_16_layers"layer {  name: "data"  type: "ImageData"  include {    phase: TRAIN  } transform_param {    #crop_size: 224    mean_value: 104    mean_value: 117    mean_value: 123    mirror: true } image_data_param {    source: "/home/satisfie/imagenet/train_new.txt"    batch_size: 8    new_height: 224    new_width: 224  }  top: "data"  top: "label"}layer {  name: "data"  type: "ImageData"  include {    phase: TEST  } transform_param {    #crop_size: 224    mean_value: 104    mean_value: 117    mean_value: 123    mirror: false } image_data_param {    source: "/home/satisfie/imagenet/val_new.txt"    batch_size: 4    new_height: 224    new_width: 224  }  top: "data"  top: "label"}layer {  bottom: "data"  top: "conv1_1"  name: "conv1_1"  type: "Convolution"  convolution_param {    num_output: 64    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv1_1"  top: "conv1_1"  name: "relu1_1"  type: "ReLU"}layer {  bottom: "conv1_1"  top: "conv1_2"  name: "conv1_2"  type: "Convolution"  convolution_param {    num_output: 64    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv1_2"  top: "conv1_2"  name: "relu1_2"  type: "ReLU"}layer {  bottom: "conv1_2"  top: "pool1"  name: "pool1"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  bottom: "pool1"  top: "conv2_1"  name: "conv2_1"  type: "Convolution"  convolution_param {    num_output: 128    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 0  }  param {    lr_mult: 0  }}layer {  bottom: "conv2_1"  top: "conv2_1"  name: "relu2_1"  type: "ReLU"}layer {  bottom: "conv2_1"  top: "conv2_2"  name: "conv2_2"  type: "Convolution"  convolution_param {    num_output: 128    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv2_2"  top: "conv2_2"  name: "relu2_2"  type: "ReLU"}layer {  bottom: "conv2_2"  top: "pool2"  name: "pool2"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  bottom: "pool2"  top: "conv3_1"  name: "conv3_1"  type: "Convolution"  convolution_param {    num_output: 256    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv3_1"  top: "conv3_1"  name: "relu3_1"  type: "ReLU"}layer {  bottom: "conv3_1"  top: "conv3_2"  name: "conv3_2"  type: "Convolution"  convolution_param {    num_output: 256    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv3_2"  top: "conv3_2"  name: "relu3_2"  type: "ReLU"}layer {  bottom: "conv3_2"  top: "conv3_3"  name: "conv3_3"  type: "Convolution"  convolution_param {    num_output: 256    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv3_3"  top: "conv3_3"  name: "relu3_3"  type: "ReLU"}layer {  bottom: "conv3_3"  top: "pool3"  name: "pool3"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  bottom: "pool3"  top: "conv4_1"  name: "conv4_1"  type: "Convolution"  convolution_param {    num_output: 512    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv4_1"  top: "conv4_1"  name: "relu4_1"  type: "ReLU"}layer {  bottom: "conv4_1"  top: "conv4_2"  name: "conv4_2"  type: "Convolution"  convolution_param {    num_output: 512    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv4_2"  top: "conv4_2"  name: "relu4_2"  type: "ReLU"}layer {  bottom: "conv4_2"  top: "conv4_3"  name: "conv4_3"  type: "Convolution"  convolution_param {    num_output: 512    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv4_3"  top: "conv4_3"  name: "relu4_3"  type: "ReLU"}layer {  bottom: "conv4_3"  top: "pool4"  name: "pool4"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  bottom: "pool4"  top: "conv5_1"  name: "conv5_1"  type: "Convolution"  convolution_param {    num_output: 512    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv5_1"  top: "conv5_1"  name: "relu5_1"  type: "ReLU"}layer {  bottom: "conv5_1"  top: "conv5_2"  name: "conv5_2"  type: "Convolution"  convolution_param {    num_output: 512    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv5_2"  top: "conv5_2"  name: "relu5_2"  type: "ReLU"}layer {  bottom: "conv5_2"  top: "conv5_3"  name: "conv5_3"  type: "Convolution"  convolution_param {    num_output: 512    pad: 1    kernel_size: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "conv5_3"  top: "conv5_3"  name: "relu5_3"  type: "ReLU"}layer {  bottom: "conv5_3"  top: "pool5"  name: "pool5"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer {  bottom: "pool5"  top: "fc6"  name: "fc6"  type: "InnerProduct"  inner_product_param {    num_output: 4096    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "fc6"  top: "fc6"  name: "relu6"  type: "ReLU"}layer {  bottom: "fc6"  top: "fc6"  name: "drop6"  type: "Dropout"  dropout_param {    dropout_ratio: 0.5  }}layer {  bottom: "fc6"  top: "fc7"  name: "fc7"  type: "InnerProduct"  inner_product_param {    num_output: 4096    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  bottom: "fc7"  top: "fc7"  name: "relu7"  type: "ReLU"}layer {  bottom: "fc7"  top: "fc7"  name: "drop7"  type: "Dropout"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "fc8"  bottom: "fc7"  top: "fc8"  type: "InnerProduct"  inner_product_param {    num_output: 1000    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }  param {    lr_mult: 1    decay_mult :1  }  param {    lr_mult: 2    decay_mult: 0  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "fc8"  bottom: "label"  top: "loss/loss"}layer {  name: "accuracy/top1"  type: "Accuracy"  bottom: "fc8"  bottom: "label"  top: "accuracy@1"  include: { phase: TEST }  accuracy_param {    top_k: 1  }}layer {  name: "accuracy/top5"  type: "Accuracy"  bottom: "fc8"  bottom: "label"  top: "accuracy@5"  include: { phase: TEST }  accuracy_param {    top_k: 5  }}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645

solver.prototxt

net: "models/vgg/train_val.prototxt"test_iter: 10000test_interval: 40000test_initialization: falsedisplay: 200base_lr: 0.0001lr_policy: "step"stepsize: 320000gamma: 0.96max_iter: 10000000momentum: 0.9weight_decay: 0.0005snapshot: 800000snapshot_prefix: "models/vgg/vgg"solver_mode: GPU
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

finetuning

模型太大，试了下，在GTX980的4G显存下，batchsize只能设置为8或者16这么小。。。 
大模型还是得服务器并行，直接在原有的模型上finetuning

VGG_ILSVRC_16_layers_deploy.prototxt

#!/usr/bin/env shset -eTOOLS=./build/toolsGLOG_logtostderr=0 GLOG_log_dir=models/vgg/Log/ \$TOOLS/caffe train \    --solver=models/vgg/solver.prototxt \    --weights models/vgg/VGG_ILSVRC_16_layers.caffemodel